CEMiTool: a Bioconductor package for performing comprehensive modular co-expression analyses

Abstract

BackgroundThe analysis of modular gene co-expression networks is a well-established method commonly used for discovering the systems-level functionality of genes. In addition, these studies provide a basis for the discovery of clinically relevant molecular pathways underlying different diseases and conditions.ResultsIn this paper, we present a fast and easy-to-use Bioconductor package named CEMiTool that unifies the discovery and the analysis of co-expression modules. Using the same real datasets, we demonstrate that CEMiTool outperforms existing tools, and provides unique results in a user-friendly html report with high quality graphs. Among its features, our tool evaluates whether modules contain genes that are over-represented by specific pathways or that are altered in a specific sample group, as well as it integrates transcriptomic data with interactome information, identifying the potential hubs on each network. We successfully applied CEMiTool to over 1000 transcriptome datasets, and to a new RNA-seq dataset of patients infected with Leishmania, revealing novel insights of the disease’s physiopathology.ConclusionThe CEMiTool R package provides users with an easy-to-use method to automatically implement gene co-expression network analyses, obtain key information about the discovered gene modules using additional downstream analyses and retrieve publication-ready results via a high-quality interactive report.Electronic supplementary materialThe online version of this article (10.1186/s12859-018-2053-1) contains supplementary material, which is available to authorized users.

Systems biology analysis of publicly available transcriptomic data reveals a critical link between AKR1B10 gene expression, smoking and occurrence of lung cancer

“…We use Venn diagrams to visualize all possible logical relations between all the DEGs between smokers and nonsmokers in all datasets evaluated. The modular analysis was performed using the Cemitool package [28]. It is based on Weighted correlation network analysis (WGCNA) and default parameters was employed (Beta Parameter = 7).…”

Systems vaccinology and big data in the vaccine development chain

“…For example, InnateDB contains data on innate immune responses 87 while INTERFEROME focuses on interferon-related processes 88 and the CEMiTool can be used for co-expression analyses and discovery of functionality of genes. 89 For the discovery and analysis of the repertoire of T-and B-cell receptor sequences, VDJviz can be applied for the routine analysis and quality control of sequencing of immune repertoires 90 and VDJdb is designed for the annotation of T-cell receptor repertoire data. 91 These databases evolve and expand continuously based on novel data, scientific insights and technologies.…”

Systems Analysis of Subjects Acutely Infected with Chikungunya Virus

“…We performed a gene co-expression network analysis using the expression profiles of all the patients and healthy controls. CEMiTool (29) identified eight coexpression modules containing 74 to over 2,000 genes (Fig. 5a).…”

“…We performed the gene co-expression analysis using the R package CEMiTool (52). For this analysis, we normalized the expression data using TMM (Trimmed Mean of M-values) and transformed it to log2 scale.…”